Attention Mechanism Based Multi-task Learning Framework for Transportation Time Prediction
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2262-4_30
Reference15 articles.
1. Fang, X., Huang, J., Wang, F., Zeng, L., Liang, H., Wang, H.: Constgat: contextual spatial-temporal graph attention network for travel time estimation at baidu maps. In: SIGKDD, pp. 2697–2705 (2020)
2. Fu, T.Y., Lee, W.C.: Deepist: deep image-based spatio-temporal network for travel time estimation. In: CIKM, pp. 69–78 (2019)
3. Hong, H., et al.: Heteta: heterogeneous information network embedding for estimating time of arrival. In: SIGKDD, pp. 2444–2454 (2020)
4. Jin, G., Wang, M., Zhang, J., Sha, H., Huang, J.: STGNN-TTE: travel time estimation via spatial-temporal graph neural network. Futur. Gener. Comput. Syst. 126, 70–81 (2022)
5. Luo, W., Tan, H., Chen, L., Ni, L.M.: Finding time period-based most frequent path in big trajectory data. In: SIGMOD, pp. 713–724 (2013)
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